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机器学习方法在商业条件下对轻型猪进行分类的适用性

Pau Salgado-López¹, Joaquim Casellas², Iara Solar Diaz³

¹Department of Animal and Food Science, Animal Nutrition and Welfare Service (SNIBA), Autonomous University of Barcelona, Bellaterra 08193, Spain.

Translational animal science

|December 19, 2024

概括

机器学习模型有效地识别出有增长迟缓风险的猪. 随机森林和增强回归优于其他方法,为猪生产效率提供了更好的决策.

科学领域:

动物科学动物科学
农业经济学农业经济学
数据科学数据科学数据科学

背景情况:

不同的猪生长率挑战了"全归全出"系统.
准确识别缓慢生长的猪对于行业的效率至关重要.

研究的目的:

评估统计方法来分类风险较大的生长迟缓的猪.
评估不同生产阶段的机器学习算法的性能.

主要方法:

分析了三个生产阶段的大数据集 (26,749头猪).
普通最小平方,决策树,随机森林和通用增强回归模型的比较.
使用曲线下的面积 (AUC) 评估分类性能.

主要成果:

随机森林和通用增强回归显示出优异的表现 (AUC 0.772-0.861).
参数线性模型具有可接受的性能 (AUC 0.752-0.818).
决策树是无效的 (AUC 0.608-0.726);出生体重和以前的体重是关键预测因素.

结论:

机器学习算法可以准确地识别患有生长迟缓风险的猪.
这些方法有助于提高生猪生产的决策和效率.
增长迟缓的预测因素因生产阶段而异.

关键词:

在曲线下面的面积.分类算法的分类算法.效率效率效率效率效率效率效率增长减缓增长减缓增长减缓活着的重量活着的重量猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪

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